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Gaussian Process Regression Analysis for Functional Data (Hardcover)
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Gaussian Process Regression Analysis for Functional Data (Hardcover)
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Gaussian Process Regression Analysis for Functional Data presents
nonparametric statistical methods for functional regression
analysis, specifically the methods based on a Gaussian process
prior in a functional space. The authors focus on problems
involving functional response variables and mixed covariates of
functional and scalar variables. Covering the basics of Gaussian
process regression, the first several chapters discuss functional
data analysis, theoretical aspects based on the asymptotic
properties of Gaussian process regression models, and new
methodological developments for high dimensional data and variable
selection. The remainder of the text explores advanced topics of
functional regression analysis, including novel nonparametric
statistical methods for curve prediction, curve clustering,
functional ANOVA, and functional regression analysis of batch data,
repeated curves, and non-Gaussian data. Many flexible models based
on Gaussian processes provide efficient ways of model learning,
interpreting model structure, and carrying out inference,
particularly when dealing with large dimensional functional data.
This book shows how to use these Gaussian process regression models
in the analysis of functional data. Some MATLAB (R) and C codes are
available on the first author's website.
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